The outcomes of elections, product sales, and the structure of social connections are all determined by the choices individuals make when presented with a set of options, so understanding the factors that contribute to choice is crucial. Of particular interest are context effects, which occur when the set of available options influences a chooser's relative preferences, as they violate traditional rationality assumptions yet are widespread in practice. However, identifying these effects from observed choices is challenging, often requiring foreknowledge of the effect to be measured. In contrast, we provide a method for the automatic discovery of a broad class of context effects from observed choice data. Our models are easier to train and more flexible than existing models and also yield intuitive, interpretable, and statistically testable context effects. Using our models, we identify new context effects in widely used choice datasets and provide the first analysis of choice set context effects in social network growth.

中文翻译：

---

在离散选择中学习可解释的特征上下文效应

选举结果、产品销售和社会关系结构都取决于个人在面对一组选项时所做的选择，因此了解促成选择的因素至关重要。特别令人感兴趣的是上下文效应，当可用选项集影响选择者的相对偏好时，就会发生上下文效应，因为它们违反了传统的理性假设，但在实践中却很普遍。然而，从观察到的选择中识别这些影响是具有挑战性的，通常需要预先了解要测量的影响。相比之下，我们提供了一种方法，用于从观察到的选择数据中自动发现一大类上下文影响。我们的模型比现有模型更容易训练、更灵活，并且还产生直观、可解释、和统计上可测试的上下文效应。使用我们的模型，我们在广泛使用的选择数据集中识别新的上下文影响，并提供社交网络增长中选择集上下文影响的首次分析。